Service Labeling Using Semi-Supervised Learning

3. The method of claim 1, wherein identifying the group of workloads based on similarities among the plurality of sets of features comprises calculating cosine similarity among the plurality of sets of features.

4. The method of claim 1, further comprising receiving input via the user interface indicating that a certain workload should be removed from the group of workloads.

5. The method of claim 1, wherein the machine learning model comprises a classification model, a tree-based model, or a linear regression model.

7. The method of claim 1, further comprising re-training the machine learning model based on the predicted label for the new workload.

10. The system of claim 8, wherein identifying the group of workloads based on similarities among the plurality of sets of features comprises calculating cosine similarity among the plurality of sets of features.

11. The system of claim 8, wherein the method further comprises receiving input via the user interface indicating that a certain workload should be removed from the group of workloads.

12. The system of claim 8, wherein the machine learning model comprises a classification model, a tree-based model, or a linear regression model.

14. The system of claim 8, wherein the method further comprises re-training the machine learning model based on the predicted label for the new workload.

17. The non-transitory computer-readable medium of claim 15, wherein identifying the group of workloads based on similarities among the plurality of sets of features comprises calculating cosine similarity among the plurality of sets of features.